RAG or Retrieval-Augmented Generation is a vital component for LLMs like ChatGPT, since it adds contextual/real time information before generating responses.
The 6 minute video (below) is all you need to have a firm understanding of the RAG concept:
Recap of the transcript/video (above):
Marina Danilevsky, a Senior Research Scientist at IBM Research, discusses the concept of Retrieval-Augmented Generation (RAG) to improve the accuracy and up-to-date nature of large language models (LLMs).
She illustrates how LLMs often lack sourcing and can provide outdated information. RAG addresses these issues by incorporating a retrieval step, where the model consults a content store (like the internet or a document collection) for relevant information before generating a response.
This approach helps keep responses current and sourced, enhancing the model’s reliability and reducing the likelihood of generating incorrect or misleading information.
Takeaways:
- LLM Limitations: Traditional LLMs can produce unsourced and outdated responses.
- RAG Framework: RAG adds a retrieval process to LLMs, where the model first consults a content store before responding to a query.
- Enhanced Accuracy: By integrating current information from the content store, RAG ensures that responses are up-to-date.
- Sourcing Information: RAG enables LLMs to provide evidence-backed answers, reducing the chances of hallucinations or misinformation.
- Admitting Uncertainty: RAG allows models to express “I don’t know” when reliable information isn’t available, avoiding the generation of potentially misleading responses.
- Continuous Improvement: Ongoing efforts at IBM and elsewhere focus on improving both the retrieval component (to provide high-quality data) and the generation aspect (for richer, more accurate responses).
This approach represents a significant advancement in the field of AI and LLMs, aiming to make interactions with these models more reliable and trustworthy.